# Comparing High-Flow Nasal Cannula and Non-Invasive Ventilation in Critical Care: Insights from Deep Counterfactual Inference

## TL;DR

This study uses a deep learning model to compare high-flow nasal cannula and non-invasive ventilation in ICU patients, showing better outcomes when treatment aligns with model predictions.

## Contribution

The novel contribution is a deep counterfactual model integrating representation learning and normalizing flows to estimate individual treatment effects in critical care.

## Key findings

- Concordant treatment at UCSD reduced odds of invasive ventilation and mortality.
- At UCI, concordant treatment was strongly linked to improved mortality outcomes.
- Model-guided strategies improve outcomes for critically ill patients.

## Abstract

Randomized trials comparing high-flow nasal cannula (HFNC) and non-invasive positive pressure ventilation (NIV) for acute respiratory failure (ARF) offer population-level guidance but often fail to capture individual variability in treatment response. In this retrospective study, we identified intensive care units (ICU) patients at risk of invasive mechanical ventilation (IMV) using a previously published risk prediction model. Patients who first received HFNC or NIV after crossing the high-risk threshold formed the early treatment cohort. We developed a deep counterfactual model that integrates representation learning, conditional normalizing flows, and confounder adjustment to estimate individualized treatment effects (ITEs) between HFNC and NIV. Treatment concordance, defined as alignment between the model’s recommendation and the treatment actually administered, was assessed using multivariate logistic regression. At UC San Diego Health (UCSD), concordant treatment was associated with significantly reduced odds of IMV (odds ratio [OR] = 0.661 for NIV; 0.677 for HFNC) and mortality or hospice discharge (OR = 0.679 for NIV; 0.749 for HFNC). At UC Irvine Health (UCI), concordant treatment was also linked to improved outcomes, particularly for mortality or hospice discharge (OR = 0.092 for NIV; 0.088 for HFNC). These findings highlight the value of individualized, model-guided respiratory support strategies in improving outcomes for critically ill patients.